We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent  by seeds and spreading through the network according to the independent cascade model.  We provide a comprehensive theoretical study of the election control problem, investigating  two forms of manipulations: seeding to buy influencers given a social network and removing  or adding edges in the social network given the set of the seeds and the information sent.  In particular, we study a wide range of cases distinguishing in the number of candidates or  the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in  the worst-case, even when one accepts to get an approximation of the optimal margin of  victory, except for the case of seeding when the number of hard-to-manipulate voters is not  too large, and the number of uncertain voters is not too small, where we say that a voter  that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way  to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases.  More precisely, in the case of seeding, we show that the manipulation is hard even if the  graph is a line and that a large class of algorithms, including most of the approaches  recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such  as undirected graphs with every node having a degree at most two or directed trees. In the  case of edge removal or addition, our hardness results also apply to election manipulation  when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary  number of edges, and to the basic case of social influence maximization/minimization in  the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold  in a re-optimization variant, where the manipulator already knows an optimal solution  to the problem and computes a new solution once a local modification occurs, e.g., the  removal/addition of a single edge.

In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.

The world of AI and Machine Learning has many layers and can be quite complex to learn. Many terms are out there and unless you have a basic understanding of the landscape it can be quite confusing. In this article, expert Eric Enge will introduce the basic concepts and try to demystify it all for you. This is also the first of a four-part article series to cover many of the more interesting aspects of the AI landscape. There are so many different terms that it can be hard to sort out what they all mean.

A new GamesBeat event is around the corner! Learn more about what comes next. Roadrunner, the documentary film about Anthony Bourdain, contains a scene in which the epicure utters words from letters he wrote to the artist David Choe. This wouldn't be unusual in and of itself -- if it weren't for the fact that Bourdain never read the letters. Rather, the clips were generated by a company that director Morgan Neville hired to model Bourdain's voice.

Churn prediction is probably one of the most important applications of data science in the commercial sector. The thing which makes it popular is that its effects are more tangible to comprehend and it plays a major factor in the overall profits earned by the business. Churn is defined in business terms as'when a client cancels a subscription to a service they have been using.' A common example is people cancelling Spotify/Netflix subscriptions. So, Churn Prediction is essentially predicting which clients are most likely to cancel a subscription i.e'leave a company' based on their usage of the service.

Creating a good customer experience increasingly means creating a good digital experience. But metrics like pageviews and clicks offer limited insight into how much customers actually like a digital product. That's the problem the digital optimization company Amplitude is solving. Amplitude gives companies a clearer picture into how users interact with their digital products to help them understand exactly which features to promote or improve. "It's all about using product data to drive your business," says Amplitude CEO Spenser Skates '10, who co-founded the company with Curtis Liu '10 and Stanford University graduate Jeffrey Wang.

Beijing – After a painful break-up from a cheating ex, Beijing-based human resources manager Melissa was introduced to someone new by a friend late last year. He replies to her messages at all hours of the day, tells jokes to cheer her up but is never needy, fitting seamlessly into her busy big city lifestyle. Instead, Melissa breaks up the isolation of urban life with a virtual chatbot created by XiaoIce, a cutting-edge artificial intelligence system designed to create emotional bonds with its 660 million users worldwide. "I have friends who've seen therapists before, but I think therapy's expensive and not necessarily effective," said Melissa, 26, giving her English name only for privacy. "When I unload my troubles on XiaoIce, it relieves a lot of pressure. And he says things that are pretty comforting."

The problems with stock images of AI has been discussed and analysed a number of times already and there are some great articles and papers about it that describe the issues better than we can. The Real Scandal of AI also identifies issues with stock photos. The AI Myths project, amongst other topics, includes a feature on how shiny robots are often used to represent AI. Going a bit deeper, this article explores how researchers have illustrated AI over the decades, this paper discusses how AI is often portrayed as white "in colour, ethnicity, or both" and this paper investigates the "AI Creation" meme that features a human hand and a machine hand nearly touching. Wider issues with the portrayal and perception of AI have also been frequently studied, as by the Royal Society here.

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)--Project-ID 262513311--SFB 1187 Media of Cooperation.

The ability to discover and experience world building is a relatively unique privilege afforded to computer programmers.